a) Field of the Invention
This invention relates to a control device for an automobile and particularly to a control device for an automobile applying neural network theory.
b) Description of the Prior Art
A pre-programmed control device currently in use with a digital computer is typically used for fuel control in an automobile. Examples are disclosed in, inter alia, U.S. Pat. Nos. 4,542,730 and 4,785,783 and in these prior art devices, a number of sensors have been used for detecting operational states of the engine, transmission, brake, vehicle height, suspension and the like. In addition, the outputs from each of these sensors are each used for a particular control variable or as a correction variable for a learning control.
In the prior art devices, however, the disadvantage exists that when using signals undergoing a complicated variation or including a great deal of noise, signal extraction precision is poor. In this respect, an engine knocking signal caused by bad timing or poor fuel consists of the signal caused by knocking and random noise caused by engine vibration and electrical noise. As a result it is extremely difficult to separate the signal due to engine knocking from the noise signal.
Also, there have been very complicated and hard-to-evaluate problems in connection with so-called sensitivity of passenger comfort, or personal preference between control variables of the automobile.
An object of this invention is to provide a control device for an automobile which at least partially mitigates the above disadvantages and problems.
An object of a feature of this invention is to provide an output signal from a sensor transformed into a plurality of signals, which are then inputted to an input layer of hierarchical neural elements, and the inputted signals therein are weighted according to weighting factors so as to generate a signal which is to be used as a parameter to determine control variables for control actuators in the automobile.
An object of another feature of this invention is that each control actuator of the automobile is controlled based on the results of the final outputs from hierarchical neural elements, an input layer of which was inputted with the output signals from a plurality of sensors.